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Data Science, AI and Deep Learning


16 Nov 2017


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The emergence of Data Science is considered to be a great advance in the domain of modelling. The abundance and the explosive growth of recorded data in recent years has added a new dimension to the established paradigms of theoretical, empirical and computational modelling; these are now augmented by data driven modelling. Data Science encompasses the established domains of data warehousing, data mining, cluster analysis, pattern classification, machine learning and data visualisation. The application of Machine Learning in general and Deep Learning in particular, to very large data sets, has led to ground-breaking progress in recognising patterns of sounds, images, & data.

The human brain finds it difficult to make sense out of very large volumes (peta bytes) of unstructured data. Yet Deep Learning can discover hidden 'connections and patterns'. Humans are not able to model these 'connections and patterns' because their cognition power limits them in making sense of these very large data sets and their inherent complexities.

This event brings together experts from industry and academe to explore and discuss the major advances and applications of these technologies.
Come along and open up to the new era of AI, Machine Learning and Deep Learning and find out how this can help you and your organisation in an increasingly 'data-driven world'.

In addition to the conference, there are four pre- and post-conference workshops:

a. In depth overview of [Deep Learning / Machine Learning and Ensemble Learning; how they relate to each other]
b. Hands on workshop on Tensorflow
c. AI in Robotics (including IOT)
d. AI in Multimedia - capturing sound, data and image

Who should attend

  • Data Scientists
  • Data Engineers
  • Machine Learning Scientists
  • Developers
  • Entrepreneurs
  • Director of Engineering
  • Big Data Analytics Experts
  • Academic researchers
  • Business Analysts
  • Chief Digital Officers
  • Chief Information Officers
  • Chief Marketing Officers
  • Chief Technical Officers
  • Data Architects
  • Data Engineers
  • Development Managers
  • Director of Engineering
  • Enterprise Architects
  • Entrepreneurs
  • Heads of Data and Analytics
  • Machine Learning Scientists
  • Venture Capitalists
  • VPs and Executives within Marketing | Development | Engineering | Technology | Sales

  • content
  • content
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